CN112785691A - Mandible defect reconstruction method, device electronic equipment and storage medium - Google Patents
Mandible defect reconstruction method, device electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112785691A CN112785691A CN202110127908.9A CN202110127908A CN112785691A CN 112785691 A CN112785691 A CN 112785691A CN 202110127908 A CN202110127908 A CN 202110127908A CN 112785691 A CN112785691 A CN 112785691A
- Authority
- CN
- China
- Prior art keywords
- mandible
- database
- defect
- normal
- mandibular
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses a mandible reconstruction method, a device, electronic equipment and a storage medium, wherein the mandible defect reconstruction method comprises the steps of obtaining CT data of a sampled person, carrying out three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme, and storing coordinates of the mandible related characteristic points to establish a normal mandible database; virtually cutting bones to obtain a mandible defect model; standardizing the mandible defect model according to the mandible related characteristic points, and searching and matching in the normal mandible database through a preset mandible database searching and matching algorithm to obtain the most similar mandible; and registering the most similar mandible obtained by searching and matching with the defected mandible model. The method provides a reference with high repeatability except doctor experience for the digital design of the transmidline mandibular defects, the second-stage mandibular defects and the bilateral asymmetric mandibular defect repair and reconstruction, and effectively solves the defects and shortcomings of the mirror image technology in clinical application.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a mandible defect reconstruction method, a mandible defect reconstruction device, electronic equipment and a storage medium.
Background
In the field of maxillofacial surgery, diseases such as tumors, trauma, inflammation and the like can cause malformation and defect of mandible, and the use of bone tissue flaps to repair the mandible is the current gold standard for treating related diseases. Currently, the main method for mandible reconstruction is the ilium flap/fibula flap, and in the mandible reconstruction operation, a major difficulty and key is to properly shape the ilium flap/fibula flap so as to reconstruct the mandible form and the mandible function correctly.
In the past, limited to the techniques and concepts at the time, the doctor shaped the fibular and iliac flaps according to his own experience, which, although enabling the reconstruction of the mandible, has evident drawbacks: the mandible reconstruction effect depends on the experience and technology of a doctor, the subjective factor influence is large, the reconstruction is not accurate enough, and the prognosis lacks stability and predictability.
In recent 10 years, the digital biotechnology has been rapidly developed, and the mandible reconstruction has benefited from this, and a new mandible reconstruction method has also been developed. At present, the flow of the most common clinical mirroring technique is as follows:
obtaining a mandible defect model by virtual osteotomy → obtaining a defect area normal shape by a mirror image technology → designing a bone repair scheme → realizing design scheme conversion by a CAD/CAM technology → performing an operation;
the method solves the problem of mandible reconstruction by the following steps: the normal form of the mandible of the defect area is recovered by utilizing the symmetry of the human body and a mirror image technology, and the bone flap of the defect area is shaped under the guidance of the normal form.
However, the method still has the defects and shortcomings: one, the mirror image technique is only applicable to the type of mandibular defect that is not midline, and for the type of mandibular defect that is midline, the method is no longer applicable. According to statistics of related documents, the proportion of the mandibular defect crossing the midline in all cases of mandibular defect in clinic is as high as 17.6%; secondly, partial patients do not reconstruct the mandible in time after tumor resection, if the mandible is in a defect state for a long time and is pulled by surrounding muscle tissues, the rest mandible can be displaced and deformed, and the mandible is difficult to be used as a reference for repairing the mandible again; thirdly, the mandible has asymmetry, and the shape of the mandible in the defect area is completely recovered by the healthy mandible on the opposite side, so that the mandible has errors. Therefore, in cases with the above problems, the mandible is reconstructed mainly by the experience of the doctor and with the digital design as an auxiliary mode, so that the uncertainty in the diagnosis and treatment process is increased.
Disclosure of Invention
The invention aims to provide a mandible defect reconstruction method, a mandible defect reconstruction device, electronic equipment and a storage medium, and provides a new method and a new process for the digital design of mandible reconstruction by using a database matching technology, an artificial intelligence technology and a machine learning technology, so that the digital design of a mandible defect case in clinic can be effectively guided.
In a first aspect, an embodiment of the present invention provides a method for reconstructing a mandibular defect, where the method includes the following steps:
acquiring CT data of a person to be sampled, performing three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme, and storing coordinates of the mandible related characteristic points to establish a normal mandible database;
virtually cutting bones to obtain a mandible defect model;
standardizing the mandible defect model according to the mandible related characteristic points, and searching and matching in the normal mandible database through a preset mandible database searching and matching algorithm to obtain the most similar mandible;
and registering the most similar mandible obtained by searching and matching with the defected mandible model.
Optionally, the selecting the feature points related to the mandible according to the preset scheme includes:
the upper alveolar edge point, the upper alveolar seat point, the upper middle incisor point, the upper cuspid point, the first molar point of the upper jaw, the lowest point of the articular nodule and the top point of the articular fossa.
The mandible database retrieval matching algorithm is as follows;
or
The algorithm is used for evaluating the similarity degree between two mandibles, wherein I represents geometrical characteristics related to the mandibles, including distance, angle and proportion, t represents a defective mandible model to be matched, c represents a normal mandible model in a database, kn represents weights of different indexes in a matching process, S is a similarity degree score between the two mandibles, S is a real number smaller than or equal to 1, the larger S represents that the similarity degree of the two mandibles is higher, and when S is equal to 1, the two mandibles are considered to be identical.
Optionally, the retrieving a match in the normal mandible database by a preset mandible database retrieval matching algorithm to obtain a most similar mandible comprises:
calculating the similarity of the mandible to be matched and the mandible in the normal mandible data by a mandible database retrieval matching algorithm;
the mandible with the highest score S was selected from all normal mandibles as the most similar mandible.
Optionally, the obtaining of the mandibular defect model by virtual osteotomy comprises:
and acquiring the CT data of the jaw face of the patient, reconstructing the mandible, and carrying out virtual mandible osteotomy according to the lesion range to obtain the mandible defect model.
Optionally, the mandible database retrieval matching algorithm is developed under the Tensorflow framework, and is trained using the normal mandible database to determine the optimal weight assignment.
In a first aspect, an embodiment of the present invention provides a mandible defect reconstruction apparatus, including:
the database establishing module is used for acquiring CT data of a sampled person, performing three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme and storing coordinates of the mandible related characteristic points to establish a normal mandible database;
the mandible defect model establishing module is used for virtually cutting bones to obtain a mandible defect model;
the retrieval matching module is used for carrying out standardized processing on the mandible defect model according to the mandible related characteristic points and retrieving and matching in the normal mandible database through a preset mandible database retrieval matching algorithm to obtain the most similar mandible;
and the registration module is used for registering the most similar mandible obtained by searching and matching with the defected mandible model.
Optionally, the retrieved matching module comprises:
the calculating unit is used for calculating the similarity of the mandible in the data of the mandible to be matched and the mandible in the normal mandible pairwise through a mandible database retrieval matching algorithm;
and the screening unit is used for selecting the mandible with the highest score S from all normal mandibles as the most similar mandible.
In a third aspect, the present invention provides an electronic device, comprising:
a processor; a memory for storing processor-executable instructions;
wherein the processor implements the above method by executing the executable instructions.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the above-described method.
Advantageous effects
The invention provides a mandible defect reconstruction method, a device, electronic equipment and a storage medium, wherein the mandible defect reconstruction method comprises the steps of obtaining CT data of a person to be sampled, carrying out three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme, and storing coordinates of the mandible related characteristic points to establish a normal mandible database; virtually cutting bones to obtain a mandible defect model; standardizing the mandible defect model according to the mandible related characteristic points, and searching and matching in the normal mandible database through a preset mandible database searching and matching algorithm to obtain the most similar mandible; and registering the most similar mandible obtained by searching and matching with the defected mandible model. The reference with high repeatability is provided for the digital design of the repair and reconstruction of the transmidline mandible defect, the second-stage mandible defect and the double-side asymmetric mandible defect, and the defects and shortcomings of the mirror image technology in clinical application are effectively overcome; a scheme for extracting the mandible framework structure is provided, and reference is provided for the subsequent related research; the artificial intelligence technology and the machine learning technology are applied to mandible reconstruction work, and the application of the artificial intelligence technology in the field of maxillofacial surgery is promoted.
Drawings
Fig. 1 is a flowchart of a mandible defect reconstruction method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for retrieving a most similar mandible from the normal mandible database by a predetermined mandible database retrieval matching algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a virtual osteotomy to obtain a mandibular defect model in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of the embodiment of the present invention, which is used to perform search matching in the normal mandible database by using a search matching algorithm to obtain the most similar mandible;
FIG. 5 is a schematic diagram of the morphology of the registration recovery defect region according to the embodiment of the present invention;
fig. 6 is a block diagram illustrating a mandibular defect reconstructing apparatus according to an embodiment of the present invention;
fig. 7 is a block diagram illustrating a retrieving and matching module in a mandible defect reconstruction apparatus according to an embodiment of the present invention;
fig. 8 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a mandible defect reconstruction method, a mandible defect reconstruction device, electronic equipment and a storage medium, and provides a new method and a new process for the digital design of mandible reconstruction by using a database matching technology, an artificial intelligence technology and a machine learning technology, so that the digital design of a mandible defect case in clinic can be effectively guided. The invention will be further described with reference to the following description and specific examples, taken in conjunction with the accompanying drawings:
it should be understood that the mandible defect reconstruction method provided by the present embodiment can be applied to hardware devices such as a controller, a personal computer or a server. Such as an ARM (advanced RISC machines) controller, an FPGA (field Programmable Gate array) controller, an SoC (System on chip) controller, a DSP (digital Signal processing) controller, or an MCU (micro controller Unit) controller; the Personal computer is, for example, a desktop computer, a notebook computer, a tablet computer, a smart phone, a smart television, a Personal Digital Assistant (PDA for short), or the like; the server may be arranged on one or more entity servers according to various factors such as functions, loads, and the like, or may be formed by a distributed or centralized server cluster, which is not limited in this embodiment.
Fig. 1 is a flowchart illustrating a method for reconstructing a mandibular defect according to an embodiment of the present invention, which includes the steps of, as shown in fig. 1:
s20, acquiring CT data of the sampled person, performing three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme, and storing coordinates of the mandible related characteristic points to establish a normal mandible database;
the CT data of the person to be sampled in this embodiment includes a population meeting the normal acquisition requirement. The persons being sampled include, but are not limited to: group, age group, said height range, no history of mandibular injuries, etc. It should be understood that the sampled person is selected in the present embodiment to obtain normal jaw CT data, so that the collected jaw CT data is more meaningful.
S40, virtually cutting bones to obtain a mandible defect model;
as shown in fig. 3, collecting a patient's maxillofacial CT, reconstructing a mandible, performing virtual mandible osteotomy with surgical design software according to a lesion range to obtain a mandible defect model, where it is to be noted that performing virtual mandible osteotomy with surgical design software to obtain a mandible defect model is a conventional technical means of a person skilled in the art and is not described herein again.
S60, standardizing the mandible defect model according to the mandible related characteristic points, and searching and matching in the normal mandible database through a preset mandible database searching and matching algorithm to obtain the most similar mandible;
specifically, according to the scheme of the mandible related characteristic points, the remaining mandible related characteristic points are selected on the mandible defect model, and the standardization processing is completed. And then, searching and matching are carried out in the normal mandible database by utilizing a preset searching and matching algorithm to obtain the most similar mandible. As shown in fig. 4.
S80, registering the most similar mandible obtained by retrieval and matching with the defected mandible model;
as shown in fig. 5, a section corresponding to the defective region in the most similar mandible is cut off for restoring the morphology of the defective region.
In the embodiment, a normal mandible database is established by acquiring CT data of a person to be sampled, performing three-dimensional reconstruction, selecting related characteristic points of the mandible according to a preset scheme and storing coordinates of the related characteristic points; virtually cutting bones to obtain a mandible defect model; standardizing the mandible defect model according to the mandible related characteristic points, and searching and matching in the normal mandible database through a preset mandible database searching and matching algorithm to obtain the most similar mandible; and registering the most similar mandible obtained by searching and matching with the defected mandible model. The method provides a reference with high repeatability except doctor experience for the digital design of the transmidline mandibular defects, the second-stage mandibular defects and the bilateral asymmetric mandibular defect repair and reconstruction, and effectively solves the defects and shortcomings of the mirror image technology in clinical application.
In some embodiments, selecting the mandible related feature points according to the preset scheme includes:
the upper alveolar edge point, the upper alveolar seat point, the upper middle incisor point, the upper cuspid point, the first molar point of the upper jaw, the lowest point of the articular nodule and the top point of the articular fossa.
The above feature points related to the mandible are key points, and one or more other feature points besides the key points can be added on the basis, as shown in the following table one:
watch 1
Specifically, the mandible database retrieval matching algorithm comprises;
or
The algorithm is used for evaluating the similarity degree between two mandibles, wherein I represents geometrical characteristics related to the mandibles, including distance, angle and proportion, t represents a mandible defect model to be matched, c represents a normal mandible model in a database, kn represents weights of different indexes in a matching process, S is a similarity grade between the two mandibles, S is a real number smaller than or equal to 1, the larger S represents that the similarity degree of the two mandibles is higher, and when the S is equal to 1, the two mandibles are considered to be identical. The specific retrieval process is to calculate the similarity of the mandible to be matched and the mandible in the normal mandible data in pairs, and select the mandible with the highest score S from all the normal mandibles as the most similar mandible. kn represents the weight of different indexes in the matching process, the mandible database retrieval matching algorithm is an artificial intelligence training algorithm developed under a Tensorflow framework, and the algorithm is trained by using a normal mandible database so as to determine the optimal weight distribution and improve the precision and the efficiency of retrieval matching.
In some embodiments, as shown in fig. 2, the retrieving a match in the normal mandible database by a preset mandible database retrieval matching algorithm to obtain a most similar mandible includes:
s601, calculating the similarity of the mandible in the data of the mandible to be matched and the normal mandible pairwise through a mandible database retrieval matching algorithm;
and S602, selecting the mandible with the highest score S from all normal mandibles as the most similar mandible.
Specifically, the virtual osteotomy obtaining the mandible defect model comprises:
and acquiring the CT data of the jaw face of the patient, reconstructing the mandible, and carrying out virtual mandible osteotomy according to the lesion range to obtain the mandible defect model.
Specifically, the mandible database retrieval matching algorithm is developed under the Tensorflow framework, and is trained using the normal mandible database to determine the optimal weight distribution.
Based on the same inventive concept, the embodiments of the present application further provide a mandible defect reconstruction device, which can be used to implement the methods described in the above embodiments, as described in the following embodiments. The principle of solving the problems of the mandible defect reconstruction device is similar to that of a mandible defect reconstruction method, so the implementation of the mandible defect reconstruction device can refer to the implementation of the mandible defect reconstruction method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Specifically, as shown in fig. 6, the mandibular defect reconstructing apparatus includes:
the database establishing module 20 is used for acquiring CT data of a sampled person, performing three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme, and storing coordinates of the mandible related characteristic points to establish a normal mandible database;
the mandible defect model establishing module 40 is used for virtually cutting bones to obtain a mandible defect model;
a retrieval matching module 60, configured to perform standardized processing on the mandible defect model according to the mandible related feature points, and retrieve and match the most similar mandible in the normal mandible database through a preset mandible database retrieval matching algorithm;
and a registration module 80 for registering the most similar mandible obtained by the retrieval and matching with the defected mandible model.
In the embodiment, the database establishing module 20 is used for acquiring the CT data of the sampled person and performing three-dimensional reconstruction, and the related characteristic points of the mandible are selected according to a preset scheme and the coordinates of the related characteristic points are stored to establish a normal mandible database; obtaining a mandible defect model by virtual osteotomy through a mandible defect model building module 40; the mandible defect model is subjected to standardized processing through a retrieval matching module 60 according to the mandible related characteristic points, and the most similar mandible is obtained through retrieval matching in the normal mandible database through a preset mandible database retrieval matching algorithm; the most similar mandible and defective mandible models obtained by retrieval and matching are registered through the registration module 80, so that a reference with high repeatability is provided for the digital design of the cross-midline mandible defect, the secondary mandible defect and the bilateral asymmetric mandible defect repair and reconstruction, except the experience of doctors, and the defects and shortcomings of the mirror image technology in clinical application are effectively solved.
Specifically, the mandible database retrieval matching algorithm is;
or
The algorithm is used for evaluating the similarity degree between two mandibles, wherein I represents geometrical characteristics related to the mandibles, including distance, angle and proportion, t represents a defective mandible model to be matched, c represents a normal mandible model in a database, kn represents weights of different indexes in a matching process, S is a similarity degree score between the two mandibles, S is a real number smaller than or equal to 1, the larger S represents that the similarity degree of the two mandibles is higher, and when S is equal to 1, the two mandibles are considered to be identical. The specific retrieval process is to calculate the similarity of the mandible to be matched and the mandible in the normal mandible data in pairs, and select the mandible with the highest score S from all the normal mandibles as the most similar mandible. kn represents the weight of different indexes in the matching process, the mandible database retrieval matching algorithm is an artificial intelligence training algorithm developed under a Tensorflow framework, and the algorithm is trained by using a normal mandible database so as to determine the optimal weight distribution and improve the precision and the efficiency of retrieval matching.
Specifically, as shown in fig. 7, the retrieved matching module 60 includes:
a calculating unit 601, configured to calculate similarity between the mandible to be matched and the mandible in the normal mandible data by using a mandible database retrieval matching algorithm;
a screening unit 602, configured to select a mandible with the highest score S from all normal mandibles as a most similar mandible.
An electronic device is also provided in the embodiments of the present application, and fig. 8 shows a schematic structural diagram of an electronic device to which the embodiments of the present application can be applied, and as shown in fig. 8, the computer electronic device includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data necessary for system operation are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The present application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the mandibular defect reconstructing apparatus in the above-mentioned embodiment; or it may be a computer-readable storage medium that exists separately and is not built into the electronic device. The computer readable storage medium stores one or more programs for use by one or more processors in performing a method of mandibular defect reconstruction as described herein.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A mandibular defect reconstruction method, comprising the steps of:
acquiring CT data of a person to be sampled, performing three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme, and storing coordinates of the mandible related characteristic points to establish a normal mandible database;
virtually cutting bones to obtain a mandible defect model;
standardizing the mandible defect model according to the mandible related characteristic points, and searching and matching in the normal mandible database through a preset mandible database searching and matching algorithm to obtain the most similar mandible;
and registering the most similar mandible obtained by searching and matching with the defected mandible model.
2. The method of claim 1, wherein the selecting the mandible defect feature points according to the predetermined scheme comprises:
the upper alveolar edge point, the upper alveolar seat point, the upper middle incisor point, the upper cuspid point, the first molar point of the upper jaw, the lowest point of the articular nodule and the top point of the articular fossa.
3. The mandibular defect reconstruction method of claim 1, wherein the mandibular database search matching algorithm is;
The algorithm is used for evaluating the similarity degree between two mandibles, wherein I represents geometrical characteristics related to the mandibles, including distance, angle and proportion, t represents a defective mandible model to be matched, c represents a normal mandible model in a database, kn represents weights of different indexes in a matching process, S is a similarity degree score between the two mandibles, S is a real number smaller than or equal to 1, the larger S represents that the similarity degree of the two mandibles is higher, and when S is equal to 1, the two mandibles are considered to be identical.
4. The method for reconstructing a mandibular defect according to claim 3, wherein said retrieving the most similar mandible that is matched in the normal mandible database by a preset mandible database retrieval matching algorithm comprises:
calculating the similarity of the mandible to be matched and the mandible in the normal mandible data by a mandible database retrieval matching algorithm;
the mandible with the highest score S was selected from all normal mandibles as the most similar mandible.
5. The mandibular defect reconstruction method of claim 4, wherein said virtually osteotomy obtaining the mandibular defect model comprises:
and acquiring the CT data of the jaw face of the patient, reconstructing the mandible, and carrying out virtual mandible osteotomy according to the lesion range to obtain the mandible defect model.
6. The mandibular defect reconstruction method of claim 3, wherein the mandibular database search matching algorithm is developed under the Tensorflow framework and is trained using the normal mandibular database to determine the optimal weight assignment.
7. A mandibular defect reconstruction device, the device comprising:
the database establishing module is used for acquiring CT data of a sampled person, performing three-dimensional reconstruction, selecting mandible related characteristic points according to a preset scheme and storing coordinates of the mandible related characteristic points to establish a normal mandible database;
the mandible defect model establishing module is used for virtually cutting bones to obtain a mandible defect model;
the retrieval matching module is used for carrying out standardized processing on the mandible defect model according to the mandible related characteristic points and retrieving and matching in the normal mandible database through a preset mandible database retrieval matching algorithm to obtain the most similar mandible;
and the registration module is used for registering the most similar mandible obtained by searching and matching with the defected mandible model.
8. The mandibular defect reconstruction device of claim 7, wherein the retrieved matching module comprises:
the calculating unit is used for calculating the similarity of the mandible in the data of the mandible to be matched and the mandible in the normal mandible pairwise through a mandible database retrieval matching algorithm;
and the screening unit is used for selecting the mandible with the highest score S from all normal mandibles as the most similar mandible.
9. An electronic device, comprising:
a processor, a memory for storing processor-executable instructions;
wherein the processor implements the method of any one of claims 1-6 by executing the executable instructions.
10. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110127908.9A CN112785691A (en) | 2021-01-29 | 2021-01-29 | Mandible defect reconstruction method, device electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110127908.9A CN112785691A (en) | 2021-01-29 | 2021-01-29 | Mandible defect reconstruction method, device electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112785691A true CN112785691A (en) | 2021-05-11 |
Family
ID=75759855
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110127908.9A Pending CN112785691A (en) | 2021-01-29 | 2021-01-29 | Mandible defect reconstruction method, device electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112785691A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256820A (en) * | 2021-05-21 | 2021-08-13 | 福州大学 | Digital developing method for mandibular surface lesion based on edge detection |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103919631A (en) * | 2014-04-28 | 2014-07-16 | 四川大学 | Manufacturing method for jaw defect individual restoration |
| CN107025678A (en) * | 2016-01-29 | 2017-08-08 | 掌赢信息科技（上海）有限公司 | A kind of driving method and device of 3D dummy models |
| CN109767841A (en) * | 2019-01-15 | 2019-05-17 | 北京大学口腔医学院 | A kind of scale model search method and device based on cranium jaw face three-dimensional configuration database |
| CN110236673A (en) * | 2019-06-10 | 2019-09-17 | 北京大学口腔医学院 | Design method and device before a kind of bilateral jaw defect Reconstruction based on database |
| CN110378941A (en) * | 2019-07-16 | 2019-10-25 | 北京大学口腔医学院 | A kind of Rigid Registration method obtaining Middle face Occluded target reference data |
| CN111265298A (en) * | 2020-03-26 | 2020-06-12 | 福建万象三维科技有限公司 | Mandible bone moving kit based on mandible three-dimensional database matching and preparation method thereof |
| CN111544079A (en) * | 2020-05-14 | 2020-08-18 | 上海交通大学医学院附属第九人民医院 | Construction method of individual mandible defect reconstruction guide plate system |
| WO2020206135A1 (en) * | 2019-04-02 | 2020-10-08 | The Methodist Hospital System | Image-based methods for estimating a patient-specific reference bone model for a patient with a craniomaxillofacial defect and related systems |
| CN112102291A (en) * | 2020-09-15 | 2020-12-18 | 北京大学口腔医学院 | Method for obtaining reference data of middle-of-surface defect target by anatomical feature point matching |
-
2021
- 2021-01-29 CN CN202110127908.9A patent/CN112785691A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103919631A (en) * | 2014-04-28 | 2014-07-16 | 四川大学 | Manufacturing method for jaw defect individual restoration |
| CN107025678A (en) * | 2016-01-29 | 2017-08-08 | 掌赢信息科技（上海）有限公司 | A kind of driving method and device of 3D dummy models |
| CN109767841A (en) * | 2019-01-15 | 2019-05-17 | 北京大学口腔医学院 | A kind of scale model search method and device based on cranium jaw face three-dimensional configuration database |
| WO2020206135A1 (en) * | 2019-04-02 | 2020-10-08 | The Methodist Hospital System | Image-based methods for estimating a patient-specific reference bone model for a patient with a craniomaxillofacial defect and related systems |
| CN110236673A (en) * | 2019-06-10 | 2019-09-17 | 北京大学口腔医学院 | Design method and device before a kind of bilateral jaw defect Reconstruction based on database |
| CN110378941A (en) * | 2019-07-16 | 2019-10-25 | 北京大学口腔医学院 | A kind of Rigid Registration method obtaining Middle face Occluded target reference data |
| CN111265298A (en) * | 2020-03-26 | 2020-06-12 | 福建万象三维科技有限公司 | Mandible bone moving kit based on mandible three-dimensional database matching and preparation method thereof |
| CN111544079A (en) * | 2020-05-14 | 2020-08-18 | 上海交通大学医学院附属第九人民医院 | Construction method of individual mandible defect reconstruction guide plate system |
| CN112102291A (en) * | 2020-09-15 | 2020-12-18 | 北京大学口腔医学院 | Method for obtaining reference data of middle-of-surface defect target by anatomical feature point matching |
Non-Patent Citations (4)
| Title |
|---|
| B. JIE ET AL.: "Post-traumatic maxillofacial reconstruction with vascularized flaps and digital techniques: 10-year experience", 《INTERNATIONAL JOURNAL OF ORAL AND MAXILLOFACIAL SURGERY》, vol. 49, no. 11, pages 1408 - 1415, XP086308050, DOI: 10.1016/j.ijom.2020.04.012 * |
| 周子疌等: "基于机器学习的颌骨特征点还原法辅助跨中线颌骨缺损重建", 《中国口腔颌面外科杂志》, no. 04, pages 323 - 327 * |
| 归来等: "数字化技术在下颌骨重建中的应用与展望", 《泸州医学院学报》, vol. 39, no. 04, pages 304 - 306 * |
| 彭歆等: "数字化外科技术在下颌骨缺损重建中的应用", 《口腔疾病防治》, vol. 25, no. 09, pages 545 - 553 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256820A (en) * | 2021-05-21 | 2021-08-13 | 福州大学 | Digital developing method for mandibular surface lesion based on edge detection |
| CN113256820B (en) * | 2021-05-21 | 2022-07-01 | 福州大学 | Digital developing method for mandibular surface lesion based on edge detection |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Torosdagli et al. | Deep geodesic learning for segmentation and anatomical landmarking | |
| Wang et al. | Skeletal maturity recognition using a fully automated system with convolutional neural networks | |
| Bernat et al. | The anatomy of the clavicle: A Three‐dimensional Cadaveric Study | |
| CN110214341A (en) | The method for rebuilding skull | |
| CN108491770A (en) | A kind of data processing method based on fracture image | |
| CN112561917A (en) | Image segmentation method and system, electronic device and readable storage medium | |
| CN116386795A (en) | Obstetrical rehabilitation data management method and system | |
| Ead et al. | Development and application of the average pelvic shape in virtual pelvic fracture reconstruction | |
| Huang et al. | Patient-specific geometrical modeling of orthopedic structures with high efficiency and accuracy for finite element modeling and 3D printing | |
| Lee et al. | Computer-Aided Design and Rapid Prototyping–Assisted Contouring of Costal Cartilage Graft for Facial Reconstructive Surgery | |
| CN111563953A (en) | Jaw bone defect reconstruction method, device, terminal and medium based on machine learning | |
| CN112785691A (en) | Mandible defect reconstruction method, device electronic equipment and storage medium | |
| Pan et al. | Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis | |
| Tian et al. | Efficient tooth gingival margin line reconstruction via adversarial learning | |
| Bakthula et al. | Automated human bone age assessment using image processing methods-survey | |
| Chen | Reconstruction individual three-dimensional model of fractured long bone based on feature points | |
| CN108618843A (en) | A kind of joint prosthesis Preoperative Method system and method based on computer aided technique | |
| CN116616893A (en) | Automatic positioning method for pelvis implant channel | |
| Xiong et al. | Virtual reconstruction of midfacial bone defect based on generative adversarial network | |
| CN111951219B (en) | Thyroid eye disease screening method, system and equipment based on orbit CT image | |
| Zeng et al. | Evaluation of ankle fractures in 228 patients from a single center using three-dimensional computed tomography mapping | |
| Hao et al. | Ai-enabled automatic multimodal fusion of cone-beam ct and intraoral scans for intelligent 3d tooth-bone reconstruction and clinical applications | |
| Qiu et al. | Biomechanical analysis of reduction malarplasty with L-shaped osteotomy | |
| CN111127636B (en) | Intelligent complex intra-articular fracture desktop-level three-dimensional diagnosis system | |
| CN112233803A (en) | Data mining device for assisting doctor in optimizing diagnosis and treatment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |